Which ensemble method adjusts weights for misclassified instances in iterative training?

  • Bagging
  • Gradient Boosting
  • Random Forest
  • K-Means Clustering
Gradient Boosting is an ensemble method that adjusts weights for misclassified instances in iterative training. It aims to correct the errors made by the previous models in the ensemble, with a focus on improving prediction accuracy. This method is particularly effective in building strong predictive models by iteratively focusing on the data points that are challenging to classify correctly.
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